This document discusses using social network analysis for software project management. It describes how social networks can help with intangibles in project management like team composition, knowledge gaps, and requirements. Social network analysis maps relationships between people, groups, and entities. It can measure centrality to understand influence. A social network approach can transform tacit knowledge to explicit knowledge for projects. The ONSOCIAL project aims to extract useful data from social networks to analyze software engineering projects and teams.

1. USING SOCIAL NETWORK
ANALYSIS FOR SOFTWARE
PROJECT MANAGEMENT
ICEMI 2014, HONG KONG 15-16 FEBRUARY 2014
PROF. PANOS FITSILIS
(FITSILIS@TEILAR.GR)
TECHNOLOGICAL EDUCATION INSTITUTE OF THESSALY

2. CONTENTS
• Contemporary Trends on Project managements
• How SNA can be used in the context of Software
Engineering and Software Project Management
• The ONSOCIAL project

3. TYPICAL PROJECT MANAGEMENT APPROACHES
• Project Management Institute – Body of Knowledge
• www.pmi.org
• Integration, scope, time, cost, quality, HR,
communication,
Process
• PRINCE
• www.prince2.com
• IPMA Competence Baseline
• www.ipma.ch
• Technical, behavioral, contextual
• Agile methods
• XP, Scrum, Crystal Reports, etc.
People

4. WHAT ARE THE INTANGIBLES IN SPM?
DEFINITION OF INTANGIBLES
The factors not shown in the traditional project analysis,
but which are of critical importance for the project and
the organization’s future success.
Using Social Networking to discover the
intangibles
How we select our team?
How we decide on our team composition?
What knowledge we are missing?
What requirements to include in our release?
Which tests to execute?

5. SOCIAL NETWORKS AND KNOWLEDGE MANAGEMENT
• Why Social Networks in KMS?
People
Knowledge
Management
Processes
Content
Knowledge Management involves people, technology, and processes in
Overlapping parts.

6. TRANSFORMING TACIT KNOWLEDGE TO EXPLICIT
KNOWLEDGE
TACIT
KNOWLEDGE
SOCIAL
NETWORK
ANALYSIS
EXPLICIT
KNOWLEDGE
Potential for
knowledge
extraction

7. Software Project Ontology
Personnel information
…
Personnel knowledge
evaluation
5
Social network
Knowledge management
Team selection

9. SOCIAL NETWORK ANALYSIS
Social network analysis [SNA] is the mapping and measuring of relationships and
flows between people, groups, organizations, computers or other
information/knowledge processing entities.
The nodes in the network are the people and groups while the links show
relationships or flows between the nodes.
We measure Social Network in terms of:
• 1. Degree Centrality:
•
The number of direct connections a node has.
• 2. Betweenness Centrality:
•
A node with high betweenness has great influence over what flows in the network
indicating important links and single point of failure.
• 3. Closeness Centrality:
•
The measure of closeness of a node which are close to everyone else.

10. DIMENSION FOR TEAM SELECTION
Projects
Roles
Locations
Model
Resources
Knowledge
Tasks
Agents

11. NETWORK OF CONNECTIONS/1

12. NETWORK OF CONNECTIONS/2

13. COLLABORATION NET FOR AGENTS
(AGENT X AGENT)
Project A
Project B
Project C

14. NETWORK THAT RELATES TEAM MEMBERS, PROJECTS AND
KNOWLEDGE
Project team members
Knowledge/skills
Projects

15. PROJECT NETWORK PRESENTING CLASSES
KNOWLEDGE AND PROJECTS
Knowledge required by each project
Knowledge/skills
Projects

16. ONSOCIAL PROJECT TECHNICAL ARCHITECTURE

17. PROBLEMS NEED TO BE ADDRESSED
• Data extraction from different Social Networks
• Sparse data usage (profiles are empty)
• Unique identification of profiles
• Creating/keeping current enterprise data corpus
• Selecting most appropriate algorithm for matching
profiles.

18. CONCLUSION
• We have presented
• How social network analysis can be used in order to
improve software project management
• Different scenarios that can help improve project analysis
• Project selection
• Team and knowledge analysis
• Requirements management
• Improving testing
• Based on solid theoretical framework (graph theory)